Building the Science of
AI Governance
Research-backed frameworks and methodologies for enterprise AI compliance, governance, and risk management. Built from real-world implementation experience.
Research Contributions
Frameworks and methodologies developed through years of enterprise AI implementation. Each addresses critical gaps in traditional approaches to AI governance.
Precision Drift Detection
Advanced methodology for detecting subtle degradation patterns in production AI systems. Goes beyond basic statistical drift to identify concept drift, performance degradation, and silent failures before they impact users.
Key Contributions
Cognitive Systems Management (CSM)
Comprehensive methodology for AI implementation that bridges strategy, technical execution, and governance. The foundational framework underlying HAIEC platform and approach to enterprise AI deployment.
Key Contributions
Red Audit Kit™
Systematic assessment framework for AI systems covering models, data pipelines, infrastructure, and governance. Provides structured methodology for identifying compliance gaps and risk exposure.
Key Contributions
LegacyShift™ Methodology
Structured approach to modernizing legacy AI systems. Addresses technical debt, compliance gaps, and operational inefficiencies while minimizing risk and maintaining business continuity.
Key Contributions
Active Research Areas
AI Regulatory Compliance
- EU AI Act implementation strategies
- Cross-jurisdiction compliance frameworks
- Automated compliance monitoring
- Policy-to-implementation mapping
Enterprise AI Governance
- Multi-model governance at scale
- Organizational governance structures
- Stakeholder alignment frameworks
- Governance automation
AI Risk Management
- Silent failure detection
- Cascading risk analysis
- Risk quantification methodologies
- Real-time risk monitoring
System Modernization
- Legacy AI migration patterns
- Technical debt assessment
- Modernization without disruption
- Compliance-preserving refactoring
Published Work
The Instruction Stack Audit Framework (ISAF): A Technical Methodology for Tracing AI Accountability Across Nine Abstraction Layers
Addresses AI accountability failures by providing a nine-layer technical specification for documenting instruction propagation from hardware to outputs. Includes 127-checkpoint audit protocol, cryptographic verification, and risk scoring based on abstraction distance. Demonstrates application to EU AI Act, NIST AI RMF, and ISO/IEC 42001 compliance requirements.
Deterministic Bias Detection for NYC Local Law 144: Why Reproducibility Matters More Than Accuracy
Presents a reproducibility-first architecture for detecting linguistic bias in job descriptions under NYC Local Law 144. Argues that regulatory compliance requires deterministic systems over probabilistic ML models. Details rule-based pattern matching, version-controlled lexicons, reproducible scoring, and cryptographic evidence generation for legally defensible documentation.
From AI Pilots to Regulatory Readiness
Practical framework for transitioning from AI experimentation to production-grade, compliant systems.
Published in: AI Governance Playbook
Why Enterprise AI Integration Strategies Fail
Systematic analysis of common architectural and organizational failures in enterprise AI adoption.
Published in: Design Bootcamp
Cognitive Systems Management: A Unified Approach
Comprehensive methodology bridging AI strategy, implementation, and governance for enterprise scale.
Published in: HAIEC Research